TURF Analysis

 TURF Analysis (Total Unduplicated Reach and Frequency) is a technique commonly used in market research to optimize product offerings by understanding the potential reach and frequency of a given set of options. It helps determine the best combination of products or services to maximize the overall reach (unique number of customers reached) and frequency (average number of products/services used per customer).

While SPSS does not have a dedicated TURF analysis tool, you can still conduct TURF-like analysis using SPSS by combining several steps manually or by using custom syntax or SPSS’s syntax editor. Here’s a guide on how to conduct a TURF analysis in SPSS:

TURF Analysis Overview

TURF analysis is most useful when you want to:

  • Identify which products or features will maximize the unique reach of customers.
  • Find out which product combinations appeal to the largest number of customers without redundancy.
  • Make decisions on product portfolios, feature selections, or marketing strategies.

Steps for Conducting a TURF Analysis in SPSS

  1. Prepare the Data:

    • Start with a dataset where each row represents an individual respondent, and each column represents a specific product or feature. Values in the cells should indicate whether a respondent is interested in or would purchase that product/feature (e.g., 1 for "interested" and 0 for "not interested").

  2. Calculate Reach for Individual Products:

    • Use Frequencies to determine the reach (the number of respondents interested) for each product or feature individually.
    • This can be done by going to Analyze > Descriptive Statistics > Frequencies and selecting the relevant variables.

  3. Calculate Reach for Product Combinations:

    • To determine the reach for combinations of products, you can create combinations of variables.
    • Use the Transform > Compute Variable function to create new variables that represent combinations of products (e.g., Product A and Product B).
    • For instance, if you want to see the reach of a combination of Product A and Product B, you could create a new variable that takes a value of 1 if a respondent is interested in either Product A or Product B and 0 otherwise.

  4. Measure Unique Reach:

    • For each combination variable, calculate the unique reach by looking at the count of respondents who would be reached by each combination without double-counting.
    • You can use Descriptive Statistics > Frequencies again to get the count of unique respondents.

  5. Analyze Frequency:

    • Frequency analysis tells you how many products or features the average respondent would likely select, helping to gauge the intensity of interest.
    • In Analyze > Descriptive Statistics > Frequencies, select multiple product variables to determine how many features are chosen by the average respondent.

  6. Optimal Combination Analysis:

    • After calculating reach and frequency for different combinations, compare them to determine the best mix of products that maximizes unduplicated reach.
    • This is typically done by examining combinations that have the highest reach with minimal overlap.

  7. Interpretation:

    • Identify combinations of products or features that provide maximum coverage without duplicating customers.
    • Based on the analysis, you can prioritize products or features that add the most incremental reach.

Example Scenario

Imagine you have data from a survey where respondents rated their interest in five different features (A, B, C, D, and E) for a new product line.

RespondentFeature AFeature BFeature CFeature DFeature E
110100
201011
311010
400101

In SPSS, you can:

  • Calculate the reach of each feature individually.
  • Create combination variables to represent groups (e.g., A + B, B + C).
  • Assess which combination has the highest unique reach without overlapping too many customers.

Limitations in SPSS for TURF Analysis

Since SPSS lacks built-in TURF capabilities, complex TURF analyses are often conducted in specialized market research software (e.g., Sawtooth, R, or Python packages). For simpler cases or small datasets, SPSS can be used effectively by following the steps above.

For advanced TURF analysis, it might be helpful to consider using other tools, as they offer more direct ways to handle and optimize for unique reach and frequency in large datasets.

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